A fuzzy set approach to fault tree and reliability analysis
Fuzzy Sets and Systems
Fuzzy system reliability analysis by interval of confidence
Fuzzy Sets and Systems
Why triangular membership functions?
Fuzzy Sets and Systems
Fuzzy system reliability analysis using fuzzy number arithmetic operations
Fuzzy Sets and Systems
Fuzzy system reliability analysis for components with different membership functions
Fuzzy Sets and Systems
System failure engineering and fuzzy methodology: an introductory overview
Fuzzy Sets and Systems - Special issue on fuzzy methodology in system failure engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Bayesian reliability analysis for fuzzy lifetime data
Fuzzy Sets and Systems
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Solving reliability redundancy allocation problems using an artificial bee colony algorithm
Computers and Operations Research
Computers and Operations Research
Fuzzy global optimization of complex system reliability
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda-Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda-Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter-oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.